Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

TL;DR

This paper introduces an online learning system for probabilistic Event Calculus theories using Answer Set Programming, enabling efficient and accurate complex event recognition in streaming data.

Contribution

It presents a novel ASP-based approach for online learning of weighted event patterns, combining probabilistic reasoning with logic programming.

Findings

Outperforms Markov Logic-based systems in efficiency and accuracy

Effective in activity recognition, maritime surveillance, and fleet management

Demonstrates superior predictive performance over batch algorithms

Abstract

Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER datasets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).